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Artificial intelligence (AI) is a fast-growing branch of computer science focused on enabling computers to perform a wide range of tasks that previously required human intelligence. Today, AI is used to power a wide range of tasks, such as image recognition, language translation, and prioritization of email or business workflows. So, if you have a smartphone, chances are you use software with AI capabilities every day.

AI is often discussed in tandem with the closely related concept of machine learning. Machine learning is the use of step-by-step processes called algorithms to allow computers to solve problems on their own - and, over time, get steadily better at doing so. Well-designed machine learning algorithms give computers the ability to solve a wide range of problems much more effectively and flexibly than if programmers had to provide detailed instructions for one specific use case.

While machine learning is used to create many simple AI applications, this approach typically requires massive, clearly-defined datasets to properly “train” the program. To create more sophisticated AI applications, an advanced type of machine learning called deep learning is used. Deep learning uses artificial neural networks that, as its name implies, are patterned after the human brain and do not require such structured datasets and human guidance to be successful. Instead, the AI application can be fed diverse, unstructured datasets and learn itself how to achieve a specified goal.

Even today’s most powerful deep learning approaches are not capable of mimicking the complexity and creativity of the human brain and its tens of billions of neurons. However, the field of artificial intelligence has made incredible strides in recent years, and is changing the way we live and work in ways that would have seemed outlandish a decade ago. Who knows what the next decade of progress in this exciting field will yield? Students learning skills in this area today may end up producing even more radical breakthroughs.‎

As artificial intelligence (AI) touches more and more areas of our daily lives, it is becoming useful to more and more career paths. Indeed, at least some background in this field is required for a growing number of jobs, and it can help give you a significant advantage over the competition in many others.

Naturally, AI and its subfields are in very high demand for popular computer science jobs. Data scientists rely on machine learning and deep learning skills in their daily work, applying various data mining techniques to both structured and unstructured big data in order to produce valuable insights for a wide variety of businesses. Skills in natural language processing (NLP) are needed to create useful chatbots for customer service as well as voice-activated assistants like Amazon’s Alexa. And advanced AI skills can put you on the cutting edge of computer programming, working on teams seeking to achieve ambitious goals like self-driving cars or autonomous robots.

A background in AI can help you in more and more jobs outside the realm of computer science, as well - it’s not much of an exaggeration to say that if a job requires human intelligence to do, artificial intelligence can help.

For example, a familiarity with machine learning can help business analysts understand and use sophisticated tools for predicting movements in the market - or develop these tools themselves. Doctors and other healthcare professionals are leveraging AI to assist with diagnosing illnesses, prescribing treatments, and analyzing medical data. Even creative professionals in visual arts and music can take advantage of AI tools to help them generate images and melodies.‎

Coursera offers online courses in an incredibly wide range of computer science topics, and artificial intelligence is no exception. If you’re a computer science student interested in this fast-growing field, online courses can give you an introduction to AI and machine learning, or help you hone your Python skills for data science. More advanced learners can dive deep, with courses and Specializations in AI engineering and deep learning. Even non-computer scientists can benefit from courses geared towards their field, such as the use of machine learning for trading and other business professionals.

Whatever your level of expertise and area of interest, online courses let you learn remotely on a flexible schedule and, typically, a lower cost than on-campus alternatives. And thanks to Coursera’s partnerships with top-ranked schools like Stanford University and Imperial College London, as well as industry leaders like IBM and Google Cloud, students can get all the advantages of online learning while still getting a high-quality education in this exciting field.‎

The skills or experience you may need to have before learning artificial intelligence (AI) includes having a solid knowledge of math, science, and computer science, specifically data science. You may want to have experience with advanced math, such as calculus and algebra, Bayesian algorithms, plus probability and statistics. In addition, a science background may be good to have for learning AI, including an understanding of physics, mechanics, cognitive learning theory, and language processing. It will also help to have a good command of computer science, including programming languages and tools such as Python, C++, and Java. Understanding the basic foundations of machine learning, deep learning, and neural networks may also be helpful to you before learning AI. If you already have some experience in software development, automotive manufacturing, and aerospace manufacturing fields, you may already have some necessary understanding of the way AI is applied in these industries.‎

The kind of people who are best suited for roles in AI are interested in highly scientific concepts and tools. People well suited to work in roles in AI feel comfortable experimenting with advanced technologies and concepts, such as machine learning, a part of AI that has given the world self-driving cars, for example. They also feel energized working with sophisticated pieces of software that can make decisions by analyzing data. In addition, the type of people well suited to work in roles in AI may want to learn to have the ability to build sophisticated pieces of equipment, such as robotics, which operate on internal software.‎

Learning artificial intelligence may be right for you if you plan on becoming an AI developer, machine learning engineer, data scientist, or research engineer or if you want your company to become better at using AI. In addition, learning AI may be beneficial if you are in the medical field, which AI is transforming when it comes to diagnosing, treating, and predicting outcomes. Learning AI may benefit you if you want to understand what AI realistically can and can't do and if you want to be able to spot opportunities to apply AI to your organization’s problems and know how to navigate the ethics of machine learning, along with other dimensions of AI.‎